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Welcome to fastai | fastai
The best way to get started with fastai (and deep learning) is to read the book, and complete the free course.. To see what's possible with fastai, take a look at the Quick Start, which shows how to use around 5 lines of code to build an image classifier, an image segmentation model, a text sentiment model, a recommendation system, and a tabular model.
Docs.fast.aiDA: 12 PA: 12 MOZ Rank: 13
fastai applications - quick start | fastai
fastai's applications all use the same basic steps and code: Create appropriate DataLoaders. Create a Learner. Call a fit method. Make predictions or view results. In this quick start, we'll show these steps for a wide range of difference applications and datasets. As you'll see, the code in each case is extremely similar, despite the very ...
Docs.fast.aiDA: 12 PA: 17 MOZ Rank: 30
Computer vision | fastai
Then we can create a Learner, which is a fastai object that combines the data and a model for training, and uses transfer learning to fine tune a pretrained model in just two lines of code: learn = vision_learner(dls, resnet34, metrics=error_rate) learn.fine_tune(1) epoch. train_loss. valid_loss.
Docs.fast.aiDA: 12 PA: 16 MOZ Rank: 30
Welcome to fastcore | fastcore
The full docs are available at fastcore.fast.ai. The code in the examples and in all fast.ai libraries follow the fast.ai style guide. In order to support interactive programming, all fast.ai libraries are designed to allow for import * to be used safely, particular by ensuring that __all__ is defined in all packages. In order to see where a ...
Fastcore.fast.aiDA: 16 PA: 16 MOZ Rank: 20
| fastai
The fastai library simplifies training fast and accurate neural nets using modern best practices. It's based on research in to deep learning best practices undertaken at fast.ai, including "out of the box" support for vision, text, tabular, and collab (collaborative filtering) models. If you're looking for the source code, head over to the fastai repo on GitHub.
Fastai1.fast.aiDA: 15 PA: 15 MOZ Rank: 20
Practical Deep Learning for Coders | Practical Deep …
At fast.ai, we have written courses using most of the main deep learning and machine learning packages used today. We spent over a thousand hours testing PyTorch before deciding that we would use it for future courses, software development, and research. PyTorch is now the world's fastest-growing deep learning library and is already used for most research …
Course.fast.aiDA: 14 PA: 14 MOZ Rank: 20
fast.ai Datasets | Practical Deep Learning for Coders
This fast.ai datasets version uses a standard PNG format instead of the platform-specific binary formats of the original, so you can use the regular data pipelines in most libraries. CIFAR100. Krizhevsky, 2009. download. This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each.
Course.fast.aiDA: 14 PA: 9 MOZ Rank: 29
Practical Deep Learning for Coders, v3 | fast.ai course v3
There is also this handy walk-thru provided by another Discourse forum (not affiliated with fast.ai). PyTorch and fastai. We teach how to train PyTorch models using the fastai library. These two pieces of software are deeply connected—you can’t become really proficient at using fastai if you don’t know PyTorch well, too. Therefore, you will often need to refer to the PyTorch docs. And ...
Course19.fast.aiDA: 16 PA: 16 MOZ Rank: 24
fastlinkcheck | fastlinkcheck
This occurs in parallel so performance is fast. Both external links and internal links are checked. Internal links are checked by verifying local files. To get started, read the documentation. Install. pip install fastlinkcheck. Usage. After installing fastlinkcheck, the cli command link_check is available from the command line. We can see various options with the …
Fastlinkcheck.fast.aiDA: 21 PA: 21 MOZ Rank: 30
fast.ai doc sprint · GitHub
Temporary community organization for fast.ai. fast.ai doc sprint has 2 repositories available. Follow their code on GitHub.
Github.comDA: 10 PA: 16 MOZ Rank: 35
fast.ai releases new deep learning course, four libraries, and 600 …
fast.ai releases new deep learning course, four libraries, and 600-page book. Written: 21 Aug 2020 by Jeremy Howard. fast.ai is a self-funded research, software development, and teaching lab, focused on making deep learning more accessible. We make all of our software, research papers, and courses freely available with no ads.
Fast.aiDA: 11 PA: 27 MOZ Rank: 48
fastai—A Layered API for Deep Learning · fast.ai
The fifth line fits the model. In this case, it is using the 1cycle policy (Smith 2018), which is a recent best practice for training and is not widely available in most deep learning libraries by default.It is annealing both the learning rates, and the momentums, printing metrics on the validation set, displaying results in an HTML table (if run in a Jupyter Notebook, or a …
Fast.aiDA: 11 PA: 50 MOZ Rank: 73
About · fast.ai
ZDNet: fast.ai's software could radically democratize AI; About the team Jeremy Howard . Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible, and is an honorary professor at the University of ...
Fast.aiDA: 11 PA: 7 MOZ Rank: 30
Welcome to nbdev | nbdev
nbdev and fastai. nbdev is a library that allows you to develop a python library in Jupyter Notebooks, putting all your code, tests and documentation in one place. That is: you now have a true literate programming environment, as envisioned by Donald Knuth back in 1983! nbdev makes debugging and refactoring your code much easier relative to ...
Nbdev.fast.aiDA: 13 PA: 13 MOZ Rank: 27
Docments | fastcore
docments [source] docments ( s, full = False, returns = True, eval_str = False) dict of parameter names to 'docment-style' comments in function or string s. The returned dict has parameter names as keys, docments as values. The return value comment appears in the return, unless returns=False. Using the add definition above, we get: docments(add)
Fastcore.fast.aiDA: 16 PA: 9 MOZ Rank: 39
GitHub - fastai/fastai: The fastai deep learning library
Learning fastai. The best way to get started with fastai (and deep learning) is to read the book, and complete the free course.. To see what's possible with fastai, take a look at the Quick Start, which shows how to use around 5 lines of code to build an image classifier, an image segmentation model, a text sentiment model, a recommendation system, and a tabular model.
Github.comDA: 10 PA: 14 MOZ Rank: 39
Setting up Search | nbdev - Fast.ai
To see what documents google is currently indexing at any particular URL, use the site: operator without any search term. e.g. "site:docs.fast.ai". You can use this to sense check what a search term ought to return as the result; If Google isn't indexing your docs site, you can ask it to do so by following these instructions.
Nbdev.fast.aiDA: 13 PA: 12 MOZ Rank: 41
Fastai :: Anaconda.org
Development: https://docs.fast.ai; Documentation: https://docs.fast.ai; 325034 total downloads Last upload: 10 days and 18 hours ago Installers. conda install noarch v2.6.3; To install this package with conda run: conda install -c fastai fastai Description. By data scientists, for data scientists. ANACONDA ...
Anaconda.orgDA: 12 PA: 14 MOZ Rank: 43
fastai_docs/gen_doc.html at master · cedrickchee/fastai_docs
Documentation source for fastai (see http://docs.fast.ai for final docs) - fastai_docs/gen_doc.html at master · cedrickchee/fastai_docs
Github.comDA: 10 PA: 50 MOZ Rank: 82
GitHub - yzuaiyou/fastai_docs: dev notebooks (for docs see …
dev notebooks (for docs see http://docs.fast.ai and https://github.com/fastai/fastai) - GitHub - yzuaiyou/fastai_docs: dev notebooks (for docs see http://docs.fast.ai ...
Github.comDA: 10 PA: 21 MOZ Rank: 50
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